Impacts of climate change on human health in humanitarian settings: Evidence gaps and future research needs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This mixed-methods study focuses on the evidence of the health impacts of climate change on populations affected by humanitarian crises, presented from the perspective of Médecins Sans Frontières (MSF)–the world’s largest emergency humanitarian medical organisation. The Sixth Assessment Report from the Intergovernmental Panel on Climate Change (IPCC) was used as the basis of a narrative review, with evidence gaps highlighted and additional literature identified relevant to climate-sensitive diseases and health problems under-reported in–or absent from–the latest IPCC report. An internal survey of MSF headquarters staff was also undertaken to evaluate the perceived frequency and severity of such problems in settings where MSF works. The findings of the survey demonstrate some discrepancies between the health problems that appear most prominently in the IPCC Sixth Assessment Report and those that are most relevant to humanitarian settings. These findings should be used to guide the direction of future research, evidence-based adaptations and mitigation efforts to avoid the worst impacts of climate change on the health of the world’s most vulnerable populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it